Social Comparisons, Status and Driving Behavior

Abstract

The establishment of desirable social norms is an integral part of a well-functioning civil society. While recent evidence has demonstrated that social comparison can affect behavior in a variety of contexts, it is not clear what type of comparative social information is most effective. Using a large-scale field experiment to study driving practices, we sent text messages containing different types of social information to drivers in Tsingtao, China. We find two types of social information to be particularly effective in reducing traffic violations: the driving behavior of those similar to oneself and the driving behavior of those with high-status cars. Our results indicate that the combination of descriptive norms with social status is a cost-effective yet powerful intervention for establishing better driving behavior in emerging markets.

A Model of Information Nudges

Abstract

A growing empirical literature has demonstrated that providing decision-makers with information (e.g. about the actions of others or the returns to di fferent actions) can aff ect behavior. However, the literature lacks a theory that can explain when such interventions will have a large effect or even the sign of the e ffect. We introduce such a theory, based on simple Bayesian updating in a setting of binary choice. It yields the following intuitive insight: the sign of the e ffect depends on whether the intervention causes the marginal agent to update her belief up or down. Further, the magnitude of the eff ect depends on both the density of agents at the margin and how much those agents' beliefs move when treated. We also show that when it is prohibitively costly or impossible to directly measure the beliefs of marginal agents, we can proxy for these beliefs with the fraction of agents taking the action in the uninformed group. Utilizing this intuition, our model makes a strong prediction about how treatment e ffect sign and magnitude will vary with the proportion taking the action in the control group. Our model reasonably rationalizes results from the literature: we perform a meta-analysis of informational nudges and that, even across very diff erent experimental settings, the magnitude of the treatment e ffect varies in a way our theory predicts

Do Beliefs About Peers Matter for Donation Matching? Experiments in the Field and Laboratory

Abstract

Charitable giving has been about 2% of US GDP since the turn of the century. A popular fundraising tool is donation matching where every dollar is matched by a third party. But field experiments find that matching does not always increase donations. This may occur because individuals believe that peer donors will exhaust the matching funds. We develop a theory of how beliefs about peers' donations affect own likelihood of donation. We test our theory using novel ``threshold match'' treatments in field and laboratory experiments. These treatments form small groups and offer a flat matching bonus if a threshold number of donations is received. One ``threshold match'' treatment more than doubles the donation rate in the field relative to no match. To better understand the mechanism behind this huge increase, we use a lab study to replicate the field results and further show that beliefs about peers' donations matter. Our theoretical, lab and field results combined suggest people are more likely to donate when they believe they are more pivotal to securing matching money. Beliefs about others matter, and should be taken into account when trying to increase donations.

The Welfare Effects of Nudges: A Case Study of Energy Use Social Comparisons

Abstract

"Nudge"-style interventions are typically evaluated on the basis of their effects on behavior, not social welfare. We use a field experiment to measure the welfare effects of one especially policy-relevant intervention, home energy conservation reports. We measure consumer welfare by sending introductory reports and using an incentive-compatible multiple price list to determine willingness-to-pay to continue the program. We combine this with estimates of implementation costs and externality reductions to carry out a comprehensive welfare evaluation. We find that this nudge increases social welfare, although traditional program evaluation approaches overstate welfare gains by a factor of five. To exploit significant individual-level heterogeneity in welfare gains, we develop a simple machine learning algorithm to optimally target the nudge; this would more than double the welfare gains. Our results highlight that
nudges, even those that are highly effective at changing behavior, need to be evaluated based on their welfare implications.